Industrial Design of Experiments

This textbook provides the tools, techniques, and industry examples needed for the successful implementation of design of experiments (DoE) in engineering and manufacturing applications. It contains a high-level engineering analysis of key issues in the design, development, and successful analysis of industrial DoE, focusing on the design aspect of the experiment and then on interpreting the results. Statistical analysis is shown without formula derivation, and readers are directed as to the meaning of each term in the statistical analysis. *Industrial Design ofExperiments: A Case Study Approach for Design and Process Optimization* is designed for graduate-level DoE, engineering design, and general statistical courses, as well as professional education and certification classes. Practicing engineers and managers working in multidisciplinary product development will find it to be an invaluable reference that provides all the information needed to accomplish a successful DoE.

Presents classical versus Taguchi DoE methodologies as well as techniques developed by the author for successful DoE Offers a step-wise approach to DoE optimization and interpretation of results Includes industrial case studies, worked examples and detailed solutions to problems.

Autorentext

Sammy G. Shina, Ph.D., P.E., is a Professor of Mechanical Engineering (ME) at the University of Massachusetts Lowell. His research interests and teaching include six sigma, design of experiments, statistical quality control, product design and manufacturing, engineering project management, and green electronics. He is the founder of the New England Lead Free Electronics Consortium, which researches, tests, and evaluates materials and processes for lead- free RoHS compliance and conversion to nanotechnology. The consortium received the EPA regional Merit award. Dr. Shina has worked and consulted for a number of technology companies developing new products and state-of-the-art manufacturing technologies.He received BS degrees in Electrical Engineering and Industrial Management from the Massachusetts Institute of Technology, an MS in Computer Science from Worcester Polytechnic Institute, and a PhD in Mechanical Engineering from Tufts University. He is the author of several books and more than 120 papers on concurrent engineering, six sigma, green design, and engineering project management.

Inhalt
Presentations, Statistical Distributions, Quality Tools and Relationship to DoE.- Samples and Populations: Statistical Tests for Significance of Mean and Variability.- Regression, Treatments, DoE Design and Modelling Tools.- Two-Level Factorial Design and Analysis Techniques.- Three-Level Factorial Design and Analysis Techniques.- DoE Error Handling, Significance and Goal Setting.- DoE Reduction Using Confounding and Professional Experience.- Multiple Level Factorial Design and DoE Sequencing Techniques.- Variability Reduction Techniques and Combining with Mean Analysis.- Strategies for Multiple Outcome Analysis and Summary of DoE Case Studies and Techniques.